upload kpi.py

kpi.py

@@ -0,0 +1,201 @@
+import os
+import time
+import math
+import sys
+import numpy as np
+import pandas as pd
+from pandas import DataFrame as df
+from sklearn.cluster import DBSCAN
+import matplotlib.pyplot as plt
+
+
+ssd_dir = str(sys.argv[1])
+file_path = '/hdd/' + ssd_dir[5:]
+count = 1
+
+def get_quad(df):
+    if df['x'] >= 0 and df['y'] >= 0:
+        return 1
+    elif df['x'] >= 0 and df['y'] < 0:
+        return 2
+    elif df['x'] < 0 and df['y'] < 0:
+        return 3
+    elif df['x'] < 0 and df['y'] >= 0:
+        return 4
+def get_inc_num(df):
+    global count
+    prev_count = 0
+    if abs(df['diff_diff']) > 0.0008:                
+        prev_count = df['inc_num']
+        df['inc_num'] = count
+        #print(prev_count)
+        return count        
+    else:
+        if prev_count != 0:            
+            count += 1
+            df['inc_num'] = 0
+            prev_count = df['inc_num']
+            return 0
+        else:
+            df['inc_num'] = 0
+            prev_count = df['inc_num']
+            return 0
+
+
+def count_inc(df):
+    e= 0
+    count  = 1
+    prev_count = 0
+    for x in df['diff_diff']:
+        if abs(x) > 8:
+            df['inc_num'][e] = count
+            prev_count = count
+            e +=1
+        else:
+            if prev_count != 0:
+                count += 1
+                df['inc_num'][e] = 0
+                prev_count = 0
+                e +=1
+            else:
+                df['inc_num'][e] = 0
+                prev_count = 0
+                e +=1
+
+def stroke_count(df):
+    e = 0
+    count = 1    
+    for x in df['diff_diff']:
+        if x > 1.2:
+            df['stroke_count'][e] = count
+            e +=1
+        else:
+            df['stroke_count'][e] = 0
+            e+=1
+            
+def get_sqrt(df):
+    e =0
+    count = 1
+    prev_x = 0.0
+    prev_y = 0.0
+    prev_z = 0.0
+    for x in range(len(df)):
+        df['sqrt'][e] = np.sqrt((df['x'][e] - prev_x) **2 + (df['y'][e] - prev_y) ** 2 +(df['z'][e] - prev_z) ** 2)
+        prev_x = df['x'][e]
+        prev_y = df['y'][e]
+        prev_z = df['z'][e]
+        e += 1
+
+        
+
+
+def dist():
+    file = os.path.join(file_path, 'coordinate.csv')
+    norm = pd.read_csv(file)
+    norm = norm.drop(norm.columns[0], axis=1)
+    norm = norm.fillna(0)
+    data = norm[['x', 'y', 'z']]
+    norm['sqrt'] = 0.0
+
+    get_sqrt(norm)
+
+    #norm = norm.apply(lambda x:np.sqrt(x) if x.name in ['sqrt'] else x, axis =1)
+    norm['diff'] = norm.diff()['sqrt']
+    norm['diff_diff'] = norm.diff()['diff']
+
+
+    norm['quad'] = norm.apply(get_quad, axis =1)
+    
+    
+    norm = norm.fillna(0)
+
+    norm['inc_num'] = 0
+    #norm['inc_num'] = norm.apply(get_inc_num, axis = 1)
+    count_inc(norm)
+    norm['stroke_count'] = 0
+    stroke_count(norm)
+    
+    print('num of inc')
+    print(norm['inc_num'].unique())
+
+    print('total stroke')
+    #print(norm['stroke_count'].unique())
+    print(norm)
+
+    
+
+    plt.plot(norm['frame'],norm['diff_diff'])
+    plt.savefig('/sources/diff_diff.png')
+
+
+
+    kpi = pd.DataFrame(index=range(0,1))
+
+    quad_count = norm[norm['stroke_count'] != 0]
+    
+    quad1_c = quad_count[quad_count['quad'] == 1]
+    quad2_c = quad_count[quad_count['quad'] == 2]
+    quad3_c = quad_count[quad_count['quad'] == 3]
+    quad4_c = quad_count[quad_count['quad'] == 4]
+    print(len(quad1_c))
+    print(len(quad2_c))
+    print(len(quad3_c))
+    print(len(quad4_c))
+    print(quad1_c)
+    kpi['PK'] = ssd_dir[5:] 
+    kpi['qaud1_c'] = len(quad1_c)
+    kpi['qaud2_c'] = len(quad2_c)
+    kpi['qaud3_c'] = len(quad3_c)
+    kpi['qaud4_c'] = len(quad4_c)
+    kpi['qaud1_s'] = quad1_c['sqrt'].mean() / 30
+    kpi['qaud2_s'] = quad2_c['sqrt'].mean() / 30
+    kpi['qaud3_s'] = quad3_c['sqrt'].mean() / 30
+    kpi['qaud4_s'] = quad4_c['sqrt'].mean() / 30
+    kpi['qaud1_d'] = quad1_c['sqrt'].mean() / 45
+    kpi['qaud2_d'] = quad2_c['sqrt'].mean() / 45
+    kpi['qaud3_d'] = quad3_c['sqrt'].mean() / 45
+    kpi['qaud4_d'] = quad4_c['sqrt'].mean() / 45
+
+
+    kpi_1 = {
+        "stroke": [len(quad1_c),len(quad2_c),len(quad3_c),len(quad4_c)],
+        "velocity": [quad1_c['sqrt'].mean() / 30, quad2_c['sqrt'].mean() / 30, quad3_c['sqrt'].mean() / 30, quad4_c['sqrt'].mean() / 30],
+        "depth": [quad1_c['sqrt'].mean() / 45, quad2_c['sqrt'].mean() / 45, quad3_c['sqrt'].mean() / 45, quad4_c['sqrt'].mean() / 45]
+    }
+
+
+    print(kpi)
+    val_list = []
+
+    #PK =
+    val_list.append(str(ssd_dir[5:-1]))
+
+    #t_stroke = 
+    val_list.append(sum(kpi_1['stroke']))
+    #t_vel
+    val_list.append("{:.4f}".format(sum(kpi_1['velocity'])/4)) 
+    #t_dep
+    val_list.append("{:.4f}".format(sum(kpi_1['depth'])/4)) 
+    #ud_s_rate =
+    val_list.append("{:.4f}".format((kpi_1['stroke'][0] + kpi_1['stroke'][3] - kpi_1['stroke'][1] - kpi_1['stroke'][2]) / sum(kpi_1['stroke'])*100))
+    #ud_v_rate =
+    val_list.append("{:.4f}".format((kpi_1['velocity'][0] + kpi_1['velocity'][3] - kpi_1['velocity'][1] - kpi_1['velocity'][2])/2))
+    #ud_d_rate =
+    val_list.append("{:.4f}".format((kpi_1['depth'][0] + kpi_1['depth'][3] - kpi_1['depth'][1] - kpi_1['depth'][2])/2))
+
+    #lr_s_rate =
+    val_list.append("{:.4f}".format((kpi_1['stroke'][2] + kpi_1['stroke'][3] - kpi_1['stroke'][0] - kpi_1['stroke'][1]) / sum(kpi_1['stroke'])*100))
+    #lr_v_rate =
+    val_list.append("{:.4f}".format((kpi_1['velocity'][2] + kpi_1['velocity'][3] - kpi_1['velocity'][0] - kpi_1['velocity'][1])/2))
+    #lr_d_rate =
+    val_list.append("{:.4f}".format((kpi_1['depth'][2] + kpi_1['depth'][3] - kpi_1['depth'][0] - kpi_1['depth'][1])/2))
+
+
+    print(val_list)
+
+
+
+
+
+
+dist()